Abstract
Some species of cover crops produce phenolic compounds with allelopathic potential. The use of math, statistical and computational tools to analyze data obtained with spectrophotometry can assist in the chemical profile discrimination to choose which species and cultivation are the best for weed management purposes. The aim of this study was to perform exploratory and discriminant analysis using R package specmine on the phenolic profile of Secale cereale L., Avena strigosa L. and Raphanus sativus L. shoots obtained by UV–vis scanning spectrophotometry. Plants were collected at 60, 80 and 100 days after sowing and at 15 and 30 days after rolling in experiment in Brazil. Exploratory and discriminant analysis, namely principal component analysis, hierarchical clustering analysis, t-test, fold-change, analysis of variance and supervised machine learning analysis were performed. Results showed a stronger tendency to cluster phenolic profiles according to plant species rather than crop management system, period of sampling or plant phenologic stage. PCA analysis showed a strong distinction of S. cereale L. and A. strigosa L. 30 days after rolling. Due to the fast analysis and friendly use, the R package specmine can be recommended as a supporting tool to exploratory and discriminatory analysis of multivariate data.
Highlights
Scanning UV–vis spectrophotometry has many advantages on the analysis of plant extracts
With the tools from the package specmine it is possible to explore the whole spectral region obtained via UV–vis spectroscopy in the dataset or to crop specific regions, depending on the compounds to be analyzed and the aim of the research
Our results suggest that intercropping species is a good option, as the sum of the phenolic compounds released by intercropped species could magnify their allelopathic potential, in addition to the physical barrier effect they play on the soil [36, 39, 41]
Summary
Scanning UV–vis spectrophotometry has many advantages on the analysis of plant extracts. Among them, it can be highlighted the small amount of sample required, easy preparation of the samples and fast data acquisition, especially that related to specific classes of secondary metabolites [1, 2], such as phenolic compounds [3,4,5]. Souza et al.: Exploratory and discriminant analysis of phenolic profiles. Most of the studies addressing these compounds aim on their quantification [9, 10], but few explore chemical data from UV–vis profiles to discriminate species according to their phenolic composition Some species of angiosperms used as cover crops, like black oat (Avena strigosa L.) and rye (Secale cereale L.), both from Poaceae family, and oilseed radish (Raphanus sativus L.) from Brassicaceae family, are known for producing phenolic compounds, related to weeds control [4,5,6,7,8].
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